Device orientation initialization

ABSTRACT

A device implementing a system for device orientation initialization includes at least one processor configured to determine that the device is within or coupled to a vehicle in motion. The at least one processor is configured to employ, in response to the determining, a first position estimation model to estimate a position of the device, and detect occurrence of a predefined condition with respect to employing the first position estimation model. The at least one processor is further configured to switch, in response to detecting occurrence of the predefined condition, from employing the first position estimation model to employing a second position estimation model to estimate the position of the device. The first and second position estimation model apply different respective error state metrics in estimating the position of the device.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/886,900, entitled “Device OrientationInitialization,” and filed on Aug. 14, 2019, the disclosure of which ishereby incorporated herein in its entirety.

TECHNICAL FIELD

The present description relates generally to estimating the positionand/or orientation of a device, including initializing deviceorientation and/or attitude.

BACKGROUND

An electronic device such as a laptop, tablet, smart phone or a wearabledevice may include a Global Navigation Satellite System (GNSS) receiverand one or more sensors (e.g., an accelerometer, a gyroscope, etc.) thatmay be used in conjunction with each other to estimate the positionand/or orientation of the electronic device.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of the subject technology are set forth in the appendedclaims. However, for purpose of explanation, several embodiments of thesubject technology are set forth in the following figures.

FIG. 1 illustrates an example positioning system in which an electronicdevice may implement the subject system for device orientationinitialization in accordance with one or more implementations.

FIG. 2 illustrates an example electronic device that may implement thesubject system for device orientation initialization in accordance withone or more implementations.

FIG. 3 illustrates an example architecture, that may be implemented byan electronic device, for device orientation initialization inaccordance with one or more implementations.

FIG. 4 illustrates a flow diagram of an example process for estimatingthe position of an electronic device in accordance with one or moreimplementations.

FIG. 5 illustrates a flow diagram of another example process forestimating the position of an electronic device in accordance with oneor more implementations.

FIG. 6 illustrates an example electronic system with which aspects ofthe subject technology may be implemented in accordance with one or moreimplementations.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. However, the subject technology is notlimited to the specific details set forth herein and can be practicedusing one or more other implementations. In one or more implementations,structures and components are shown in block diagram form in order toavoid obscuring the concepts of the subject technology.

An electronic device may include a GNSS receiver and an inertialmeasurement unit (IMU) (e.g., including one or more sensors such as anaccelerometer and/or a rate gyroscope) that may be used alone or inconjunction with each other to estimate the position and/or orientationof the electronic device. The device attitude (or orientation) mayinclude one or more parameters, including heading (e.g., angle relativeto magnetic north), pitch (e.g., motion about the lateral axis) and roll(e.g., motion about the longitudinal axis).

With respect to heading, the electronic device may further include amagnetometer configured to indicate device heading. However, themagnetometer may be prone to noise errors, such that its estimatedheading is inaccurate. Accelerometer measurements (e.g., by a deviceaccelerometer) may also be used to determine device heading, forexample, based on performing principal component analysis on theaccelerometer data. However, determining device heading based onprincipal component analysis may sometimes result in heading ambiguity.For example, the estimated heading may be ambiguous with respect towhether the estimate is in the forward direction or the backwarddirection (e.g., the estimated device heading may off by 180°).

The electronic device may implement a Kalman filter, which usesinformation provided by the GNSS receiver and/or the IMU to estimatedevice position. In doing so, the Kalman filter may be provided with aninitial set of device orientation parameters. In a case where the deviceis within or coupled to a moving vehicle, an inaccurate set oforientation parameters may result in delay with respect to GNSS receiverestimates converging with IMU estimates, potentially resulting inprolonged errors in device position estimation by the Kalman filter.

The subject system provides for initially using a first positionestimation model, in conjunction with the Kalman filter, to estimatedevice position. The first position estimation model may correspond to alow accuracy and high acceleration model (e.g., generally used to trackpedestrian motion). After the occurrence of a predefined condition(e.g., passage of a preset period of time, and/or convergence of GNSSreceiver estimates and IMU estimates), the device switches to using asecond position estimation model (e.g., corresponding to higher accuracyand lower acceleration, generally used to track motion of a vehicle), inconjunction with the Kalman filter, to estimate device position. Thefirst and second position estimation models may apply differentrespective error state metrics (e.g., associated with the IMU). Asnoted, the first position estimation model may correspond with loweraccuracy and higher acceleration relative to the second positionestimation model. By virtue of using the first and second positionestimation models in this manner (e.g., as the first position estimationmodel may be less prone to inverting device heading), it is possible todecrease heading ambiguity with respect to determining initial deviceorientation (attitude) parameters for estimating device position.

The subject system may further provide for initializing a direction oftravel for the device, where the direction of travel is used toinitialize the device orientation parameters for the above-mentionedfirst position estimation model. In some cases where the device ismounted or otherwise coupled to a moving vehicle, accelerationattributed to centrifugal motion may result in inaccurate estimates forthe direction of travel, and use of these inaccurate estimates by theKalman filter may result in inaccurate estimates for device position.

The electronic device may determine that acceleration measurements(e.g., provided by a device accelerometer) meet a predefined condition,or threshold value(s), associated with centrifugal motion. If thepredefined condition is met, the device may calculate a direction oftravel for the device based on a kinematics acceleration model whichimplements motion-based constraints (e.g., non-holonomic constraintsthat constrain vehicle motion in the vertical and/or sidewaysdirections) with respect to the direction of travel. The calculateddirection of travel may be used to estimate device orientation, whichmay be used as an initial (and/or subsequent) set of parameters for theabove-noted first position estimation model.

Thus, it is possible for the subject system to determine initial sets ofparameters for device orientation (e.g., with reduced ambiguity withrespect to heading and/or direction of travel). The device orientationmay be used as initial value(s) for a Kalman filter to estimate deviceposition.

FIG. 1 illustrates an example positioning system 100 in which anelectronic device 102 may implement the subject system for deviceorientation initialization in accordance with one or moreimplementations. Not all of the depicted components may be used in allimplementations, however, and one or more implementations may includeadditional or different components than those shown in the figure.Variations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the claims as set forthherein. Additional components, different components, or fewer componentsmay be provided.

The positioning system 100 includes the electronic device 102 and GNSSsatellites 106 a, 106 b, 106 c and 106 d, hereinafter “106 a-106 d”. Forexplanatory purposes, the positioning system 100 is illustrated in FIG.1 as including the electronic device 102 and the four GNSS satellites106 a-106 d; however, the positioning system 100 may include any numberof electronic devices and any number of GNSS satellites. Otherpositioning technologies may be used independent of or in conjunctionwith GNSS technology to determine device position. Examples of suchpositioning technologies include, but are not limited to, Wi-Fipositioning, cellular phone signal positioning, Bluetooth signalpositioning and/or image recognition positioning.

The positioning system 100 may be used for estimating the position ofthe electronic device 102. In one or more implementations, theelectronic device 102 is contained within and/or mounted or otherwisecoupled to a vehicle 104 in motion, and the position of the electronicdevice 102 is continuously updated while the vehicle 104 is in motion(e.g., for the duration of a trip). The vehicle 104 may be a landvehicle such as an automobile, a motorcycle, a bus, a train, a bicycleor a skateboard, or may be a watercraft, an aircraft or any otherappropriate vehicle for travel. In FIG. 1 , by way of example, thevehicle 104 is depicted as an automobile.

The positioning system 100 allows the electronic device 102 to determineits position based at least in part on signals received from GNSSsatellites 106 a-106 d. For example, the positioning system 100 allowsthe electronic device 102 to determine its position (e.g., longitude,latitude, and altitude/elevation) using signals transmitted along a lineof sight by radio from GNSS satellites 106 a-106 d. For example, theGNSS satellites 104 a-104 d may be compatible with one or more of theGlobal Positioning System (GPS), the Globalnaya NavigazionnayaSputnikovaya Sistema (GLONASS), the Galileo positioning system, and/orgenerally any positioning system.

Other positioning technologies (not shown) may be used independent of orin conjunction with GNSS (e.g., the GNSS satellites 104 a-104 d) todetermine device position. For example, the location of the electronicdevice 102 may be determined based on time of arrival, angle of arrival,and/or signal strength of signals received from wireless access pointswhich may have known locations (e.g., within a building or store,mounted on street posts, etc.). Alternatively or in addition,positioning technologies such as, but not limited to, cellular phonesignal positioning, (e.g., positioning using cellular network and mobiledevice signals), indoor positioning systems, Bluetooth signalpositioning and/or image recognition positioning may be used todetermine device position.

Moreover, the electronic device 102 may implement an inertial navigationsystem (INS). The INS may use device sensor(s) (e.g., motion sensorssuch as accelerometers and/or rate gyroscopes) to calculate device state(e.g., device position, velocity, orientation) for supplementingposition data provided by the above-mentioned positioning technologiesin order to estimate device position.

The electronic device 102 may be, for example, a portable computingdevice such as a laptop computer, a smartphone, a peripheral device(e.g., a digital camera, headphones), a tablet device, a wearable devicesuch as a watch, a band, and the like, or any other appropriate devicethat includes, for example, one or more wireless interfaces, such asGNSS radios, WLAN radios, cellular radios, Bluetooth radios, Zigbeeradios, near field communication (NFC) radios, and/or other wirelessradios. In FIG. 1 , by way of example, the electronic device 102 isdepicted as a smartphone. The electronic device 102 may be, and/or mayinclude all or part of, the electronic device discussed below withrespect to FIG. 2 , and/or the electronic system discussed below withrespect to FIG. 6 .

FIG. 2 illustrates an example electronic device that may implement thesubject system for device orientation initialization in accordance withone or more implementations. For explanatory purposes, FIG. 2 isprimarily described herein with reference to the electronic device 102of FIG. 1 . Not all of the depicted components may be used in allimplementations, however, and one or more implementations may includeadditional or different components than those shown in the figure.Variations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the claims as set forthherein. Additional components, different components, or fewer componentsmay be provided.

The electronic device 102 may include a host processor 202, a memory204, one or more sensor(s) 206, positioning circuitry 208 and acommunication interface 210. The host processor 202 may include suitablelogic, circuitry, and/or code that enable processing data and/orcontrolling operations of the electronic device 102. In this regard, thehost processor 202 may be enabled to provide control signals to variousother components of the electronic device 102. The host processor 202may also control transfers of data between various portions of theelectronic device 102. The host processor 202 may further implement anoperating system or may otherwise execute code to manage operations ofthe electronic device 102.

The memory 204 may include suitable logic, circuitry, and/or code thatenable storage of various types of information such as received data,generated data, code, and/or configuration information. The memory 204may include, for example, random access memory (RAM), read-only memory(ROM), flash, and/or magnetic storage.

In one or more implementations, the memory 204 may store values forsensor signal measurements, GNSS receiver data, device positionestimates and/or device orientation (attitude) estimates, for example,based on motion of the electronic device 102. The memory 204 may alsostore component(s) and/or module(s) configured to estimate deviceposition, for example, as discussed with respect to FIG. 3 as discussedbelow.

The sensor(s) 206 may include one or more motion sensor(s), such as anaccelerometer and/or a gyroscope (e.g., a rate gyroscope). The motionsensor(s) may be used to facilitate movement and orientation relatedfunctions of the electronic device 102, for example, to detect movement,direction, and orientation of the electronic device 102.

Alternatively or in addition, the sensor(s) 206 may include one or moreof a barometer, an electronic magnetometer, an image sensor, orgenerally any sensor that may be used to facilitate a positioningsystem. The barometer may be utilized to detect atmospheric pressure,for use in determining altitude change of the electronic device 102. Theelectronic magnetometer (e.g., an integrated circuit chip) may providedata used to determine device heading (e.g., the direction of magneticnorth), for example to be used as part of a digital compass. The imagesensor (e.g., a camera) may be used to capture images (e.g.,photographs, video) to derive position and/or sequences of images toderive device motion. Captured single images and/or sequences of imagesmay also be used to derive orientation of the image sensor (e.g., and/orelectronic device 102).

The positioning circuitry 208 may be used in determining the position ofthe electronic device 102 based on positioning technology. For example,the positioning circuitry 208 may provide for one or more of GNSSpositioning (e.g., via a GNSS receiver configured to receive signalsfrom the GNSS satellites 104 a-104 d), wireless access point positioning(e.g., via a wireless network receiver configured to receive signalsfrom wireless access points), cellular phone signal positioning,Bluetooth signal positioning (e.g., via a Bluetooth receiver), imagerecognition positioning (e.g., via an image sensor) and/or an INS (e.g.,via motion sensors such as an accelerometer and/or gyroscope).

The communication interface 210 may include suitable logic, circuitry,and/or code that enables wired or wireless communication, such asbetween the electronic device 102 and other device(s). The communicationinterface 210 may include, for example, one or more of a Bluetoothcommunication interface, an NFC interface, a Zigbee communicationinterface, a WLAN communication interface, a USB communicationinterface, or generally any communication interface.

In one or more implementations, one or more of the host processor 202,the memory 204, the sensor(s) 206, the positioning circuitry 208, thecommunication interface 210, and/or one or more portions thereof, may beimplemented in software (e.g., subroutines and code), may be implementedin hardware (e.g., an Application Specific Integrated Circuit (ASIC), aField Programmable Gate Array (FPGA), a Programmable Logic Device (PLD),a controller, a state machine, gated logic, discrete hardwarecomponents, or any other suitable devices) and/or a combination of both.

FIG. 3 illustrates an example architecture 300, that may be implementedby an electronic device, for device orientation initialization inaccordance with one or more implementations. Not all of the depictedcomponents may be used in all implementations, however, and one or moreimplementations may include additional or different components thanthose shown in the figure. Variations in the arrangement and type of thecomponents may be made without departing from the spirit or scope of theclaims as set forth herein. Additional components, different components,or fewer components may be provided.

In one or more implementations, the architecture 300 may provide fordetermining estimates of device orientation (e.g., heading and/ordirection of travel). The estimates of device orientation may be used asinitial values to a Kalman filter (e.g., the Kalman filter 316) toestimate device position.

The architecture 300 may include a GNSS receiver 302. For example, theGNSS receiver 302 may provide for GNSS positioning (e.g., correspondingto the positioning circuitry 208 in FIG. 2 ). The architecture 300 mayfurther include an inertial navigation system (INS) 304, which mayinclude an inertial measurement unit (IMU) 306 with an accelerometer 308and/or a gyroscope 310 (e.g., a rate gyroscope). For example, one ormore of the accelerometer 308 and/or the gyroscope 310 may correspond tothe sensor(s) 206 in FIG. 2 .

The architecture 300 may further include a position estimator 312configured to output an estimated position 322 for the electronic device102. The position estimator 312 may include a device orientationinitialization module 314 and the Kalman filter 316, which includes afirst position estimation model 318 and a second position estimationmodel 320. For explanatory purposes, FIG. 3 illustrates a single GNSSreceiver 302, a single accelerometer 308 and a single gyroscope 310;however, any number of GNSS receivers, accelerometers, gyroscopes,magnetometers, and/or other sensors may be used.

In one or more implementations, the IMU 306 may be configured to measurelinear and angular motion of the electronic device 102, for example,based on measurements received from the accelerometer 308 and/or thegyroscope 310. The IMU 306 may be part of the INS 304, which uses themeasured linear and angular motion from the IMU 306 to estimate devicestate (e.g., position, velocity, attitude).

The position estimator 312 (e.g., including the Kalman filter 316) mayreceive output from the GNSS receiver 302 (e.g., corresponding toestimated device position(s)), as well as output from the INS 304 (e.g.,corresponding to estimated device state(s)) as input. The positionestimator 312 may be configured to provide the estimated position 322 ofthe electronic device 102 as output. In one or more implementations, theKalman filter 316 may correspond to an algorithm that uses a series ofmeasurements/signals (e.g., which may contain noise and otherinaccuracies) observed over time, and that produces estimates of unknownvariables (e.g., device state and/or position) which tend to be moreaccurate than those based on a single measurement alone (e.g., singleGNSS measurements). Thus, the Kalman filter 316 may be configured to usethe respective estimates as provided by the GNSS receiver 302 and theINS 304 in determining the estimated position 322.

As noted above, device orientation (or attitude) may include one or moreparameters, including heading (e.g., angle relative to magnetic north),pitch (e.g., motion about the lateral axis) and roll (e.g., motion aboutthe longitudinal axis). A magnetometer (e.g., one of the sensor(s) 206)of the electronic device 102 may be configured to estimate deviceheading, but may be prone to noise errors. Further, accelerometermeasurements (e.g., provided by the accelerometer 308 and/or IMU 306)may also be used to estimate device heading based on performingprincipal component analysis on the accelerometer data. However, theestimated device heading may be ambiguous with respect to whether theestimate is in the forward direction or the backward direction (e.g.,the estimated device heading may off by 180°). In a case where theelectronic device 102 is within or coupled to a moving vehicle (e.g.,the vehicle 104), an inaccurate set of orientation parameters (e.g.,including a device heading error of 180°) may result in delay withrespect to GNSS receiver estimates converging with IMU/INS estimates,thereby likely resulting in prolonged errors in device positionestimation provided by the Kalman filter 316.

In one or more implementations, the electronic device 102 may beconfigured to detect if the device is within and/or mounted or otherwisecoupled to a vehicle in motion (e.g., the vehicle 104). In doing so, theelectronic device 102 may use one or more signals to detect travelwithin a vehicle, including but not limited to: a signal from anapplication (e.g., a map-based application and/or travel application)indicating vehicle motion (e.g., via automobile, train, bus); and/or asignal from a motion classifier (e.g., which may be implemented by analways-on processor of the electronic device 102) indicating vehiclemotion.

After detecting that the electronic device 102 is within and/or mountedor coupled to a vehicle in motion, the Kalman filter 316 may begin withusing the first position estimation model 318 to estimate deviceposition. Further, after the occurrence of a predefined condition(discussed below), the Kalman filter 316 may switch to using the secondposition estimation model 320 to estimate device position. In general,the first position estimation model 318 may correspond to a low accuracyand higher acceleration model (e.g., which may be used to trackpedestrian motion), while the second position estimation model 320 maycorrespond to a high accuracy and low acceleration model (e.g., whichmay be used to track vehicle motion). For example, the first positionestimation model 318 may be less prone to providing an inverted heading(e.g., an estimated device heading off by 180°).

In one or more implementations, the first position estimation model 318may use error state metrics (e.g., associated with the IMU 306 and/orINS 304) that are different than those used by the second positionestimation model 320. The first position estimation model 318 may setgyroscope bias (e.g., on a per axis basis, for 3 axes), accelerometerscale factors (e.g., on a per axis basis) and/or accelerometer bias(e.g., on a per axis basis) as provided by the IMU 306 to correspondwith low accuracy and high acceleration (e.g., relative to the secondposition estimation model 320). As noted above, the first positionestimation model 318 may generally be used to track pedestrian motion,where high acceleration events (e.g., motion from walking, jogging andthe like) are more likely to occur relative to motion of a devicemounted in a vehicle.

Moreover, the second position estimation model 320 may set gyroscopebias (e.g., on a per axis basis, for 3 axes), accelerometer scalefactors (e.g., on a per axis basis) and/or accelerometer bias (e.g., ona per axis basis) as provided by the IMU 306 to correspond with highaccuracy and low acceleration (e.g., relative to the first positionestimation model 318). As noted above, the second position estimationmodel 320 may generally be used to track vehicle motion, where highacceleration events are less likely to occur relative to pedestrianmotion.

As noted above, the Kalman filter 316 may switch from using the firstposition estimation model 318 to using the second position estimationmodel 320 in response to detecting a predefined condition. In one ormore implementations, the predefined condition may correspond to use ofthe first position estimation model 318 for a predetermined period oftime (e.g., 30 seconds, 60 seconds, 100 seconds, 150 seconds, etc.). Forexample, the predetermined period of time may correspond to a time(e.g., based on empirical data) after which GNSS receiver estimateswould generally converge with IMU estimates. In this manner, theelectronic device 102 would not necessarily be required to perform theadditional processing to detect such convergence. Alternatively or inaddition, the predefined condition may correspond to the electronicdevice 102 determining that the GNSS receiver estimates have convergedwith the IMU estimates (e.g., based on predefined heuristics indicatingsufficient convergence).

By virtue of initially using the first position estimation model 318,and switching to using the second position estimation model 320, it ispossible to decrease ambiguity in heading estimates. With reducedambiguity for heading, initial device orientation parameters may be morereliable when estimating device position (e.g., via the Kalman filter316).

In one or more implementations, after the Kalman filter has alreadyswitched from using the first position estimation model 318 to using thesecond position estimation model 320 based on detecting the predefinedcondition, the electronic device 102 may be configured to switch back tousing the first position estimation model 318. The electronic device 102may be configured to detect the occurrence of a second predefinedcondition, and perform such switching back in response to detecting thesecond predefined condition. For example, the second predefinedcondition may correspond to the electronic device 102 being removed ordismounted and/or otherwise adjusted/moved (e.g., based on a thresholdamount of movement) with respect to its fixed position within thevehicle 104.

Similar to the initialization process, using the first positionestimation model 318 in this scenario (e.g., dismount and/or adjustmentfrom fixed position) may provide for reducing ambiguity for estimatedheading (e.g., reducing heading estimates related to 180° errors).Moreover, the Kalman filter 318 may use the first position estimationmodel 318 until the (first) predefined condition occurs again (e.g.,passage of the predetermined period of time and/or detecting convergenceof convergence of the GNSS receiver estimates and IMU estimates), andagain switch to using the second position estimation model 320.

As noted above, acceleration attributed to centrifugal motion may resultin inaccurate estimates for the direction of travel. For example,backing out of a driveway while turning may produce accelerometermeasurements which cause the direction of travel to be inaccurate to theorder of 90°. Moreover, the direction of travel corresponds to deviceheading, which may be a component of device orientation. As such, theestimated initial device orientation values provided by the INS 304 tothe Kalman filter 316 may be inaccurate, resulting in degraded deviceposition estimates.

In one or more implementations, the device orientation initializationmodule 314 may be configured to reduce error(s) associated withacceleration attributed to centrifugal (and/or centripetal) motion.Although illustrated in the example of FIG. 3 as being separate andoutside of the Kalman filter 316, the device orientation initializationmodule 314 may instead by included within the Kalman filter 316 in oneor more implementations or may correspond to a module outside of theposition estimator 312. The device orientation initialization module 314may be configured to set a direction of travel (e.g., corresponding todevice heading) based on acceleration measurements provided by theaccelerometer 308 (e.g., measurements captured over a rolling,predefined time period such as 3.5 seconds). The initial direction oftravel may be used to determine the device orientation parameters forinitializing the first position estimation model 318.

In particular, the device orientation initialization module 314 maydetermine that the acceleration measurements (e.g., provided by theaccelerometer 308) meet a predefined condition (and/or thresholdvalue(s)) associated with centrifugal motion (e.g., based on empiricaldata associated with backing out of a driveway, taking sharp turns, andthe like). If the predefined threshold is met, the device orientationinitialization module 314 may set the direction of travel based on akinematics acceleration model (discussed below) which implementsmotion-based constraints with respect to the direction of travel.Otherwise, the device orientation initialization module 314 may set thedirection of travel based on a primary direction of acceleration (e.g.,as provided by the accelerometer 308) for the electronic device 102. Thedirection of travel as set by the device orientation initializationmodule 314 may be used to estimate device orientation (or attitude),which may be used as an initial set of parameters for the first positionestimation model 318.

As noted, the kinematics acceleration model may implement motion-basedconstraints. For example, the motion-based constraints may correspond toone or more non-holonomic constraints which constrain motion in thevertical and/or in the sideways directions (e.g., as these directionsare limited for the vehicle 104, which typically travels in the forwardor backward direction). In one or more implementations, acceleration fora motion of the vehicle (e.g., the vehicle 104) may generally becalculated as follows:

$\begin{matrix}{{acceleration}\begin{matrix}{= {{a*{es}} + {\frac{s^{2}}{r}*{en}}}} \\{= {{a*{es}} + {w*s*{en}}}}\end{matrix}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

In the example of Equation (1), acceleration corresponds to accelerationperpendicular to gravity if acceleration is near constant, or a leadsingular vector if acceleration is spread out in a dominant direction.In addition, a corresponds to a derivative of speed s, w corresponds toangular rotation rate about the vertical axis, es corresponds to thedirection of travel, and en corresponds to a vector toward the center ofrotation.

With motion-based constraints, an assumption may be made that thedirection of travel is not in the vertical direction ev. Therefore:en=ev×es  (Equation 2)

Further, acceleration may be calculated as follows:

$\begin{matrix}\begin{matrix}{{acceleration} = {{a*{es}} + {w*s*\left( {{ev}\mspace{14mu} x\mspace{9mu}{es}} \right)}}} \\{= {{a*I*{es}} + {s*w*\left\lbrack {{ev}\mspace{14mu} x} \right\rbrack*{es}}}} \\{= {\left( {{a*I} + {s*w*\left\lbrack {{ev}\mspace{14mu} x} \right\rbrack}} \right)*{es}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

In the example of Equation (3), I corresponds to the 3×3 identity matrixand [ev x] corresponds to the skew-symmetric cross product matrix, suchthat the matrix product [ev x]*es is equal to the vector cross productcross(ev, es).

Moreover, the matrix (a*I+s*w*[ev x]) can be inverted to solve fordirection of travel es as follows:es=inv(a*I+s*w*[ev x])*acceleration  (Equation 4)

Thus, the device orientation initialization module 314 may set thedirection of travel es based on Equation (4), in case where thepredefined threshold for acceleration (e.g., associated with centrifugalmotion) is met. If the predefined threshold for acceleration is not met,the device orientation initialization module 314 may set the directionof travel es based on a primary direction of acceleration (e.g., asprovided by the accelerometer 308) for the electronic device 102.Further, the direction of travel may be used to estimate deviceorientation, which may be used as an initial set of parameters for thefirst position estimation model 318.

By virtue of setting the direction of travel in this manner, it ispossible for the electronic device 102 to determine initial sets ofparameters for device orientation (e.g., with reduced ambiguity withrespect to heading and/or direction of travel). The device orientationmay be used as initial value(s) for the Kalman filter 316 to estimatedevice position.

In one or more implementations, one or more of components of the GNSSreceiver 302, the INS 304, the IMU 306, the accelerometer 308, thegyroscope 310, the position estimator 312, the device orientationinitialization module 314, the Kalman filter 316, the first positionestimation model 318, and/or the second position estimation model 320are implemented as software instructions, stored in the memory 204,which when executed by the host processor 202, cause the host processor202 to perform particular function(s).

In one or more implementations, one or more of components of the GNSSreceiver 302, the INS 304, the IMU 306, the accelerometer 308, thegyroscope 310, the position estimator 312, the device orientationinitialization module 314, the Kalman filter 316, the first positionestimation model 318, and/or the second position estimation model 320may be implemented in software (e.g., subroutines and code), hardware(e.g., an Application Specific Integrated Circuit (ASIC), a FieldProgrammable Gate Array (FPGA), a Programmable Logic Device (PLD), acontroller, a state machine, gated logic, discrete hardware components,or any other suitable devices), and/or a combination of both. In one ormore implementations, some or all of the depicted components may sharehardware and/or circuitry, and/or one or more of the depicted componentsmay utilize dedicated hardware and/or circuitry. Additional features andfunctions of these modules according to various aspects of the subjecttechnology are further described in the present disclosure.

FIG. 4 illustrates a flow diagram of an example process for estimatingthe position of an electronic device in accordance with one or moreimplementations. For explanatory purposes, the process 400 is primarilydescribed herein with reference to the electronic device 102 of FIG. 1 .However, the process 400 is not limited to the electronic device 102 andone or more blocks (or operations) of the process 400 may be performedby one or more other components of the electronic device 102, and/or byother suitable devices. Further for explanatory purposes, the blocks ofthe process 400 are described herein as occurring in serial, orlinearly. However, multiple blocks of the process 400 may occur inparallel. In addition, the blocks of the process 400 need not beperformed in the order shown and/or one or more blocks of the process400 need not be performed and/or can be replaced by other operations.

At block 402, the electronic device 102 determines that it is positionedwithin or coupled to a vehicle in motion (e.g., the vehicle 104). Theelectronic device 102 selects, in response to the determining, to use afirst position estimation model to estimate a position of the electronicdevice 102 (404). Using the first position estimation model may providefor initializing an orientation for the electronic device 102, theorientation including heading of the electronic device 102. In one ormore implementations, using the first position estimation model may bebased on an initial heading of the electronic device 102, the initialheading of the electronic device 102 having been determined based on akinematics acceleration model which implements at least one motion-basedconstraint.

The electronic device 102 detects a predefined condition with respect tousing a first position estimation model (e.g., the first positionestimation model 318) to estimate the position of the electronic device102 (406). Detecting the predefined condition may correspond withdetecting that a predetermined amount of time has passed with respect tousing the first position estimation model to estimate the position ofthe electronic device 102. Alternatively or in addition, detecting thepredefined condition may correspond with detecting that GNSSmeasurements corresponding to device position converge with Kalmanfilter output corresponding to device position.

The electronic device 102 selects, in response to detecting thepredefined condition, to switch from using the first position estimationmodel to using a second position estimation model (e.g., the secondposition estimation model 320) to estimate the position of theelectronic device 102 (408). The first and second position estimationmodels may apply different respective error state metrics to estimatethe position of the electronic device 102.

Each of the first and second position estimation models may beconfigured to estimate the position of the electronic device 102 basedon Global Navigation Satellite System (GNSS) measurements. Each of thefirst and second position estimation models may be implemented within aKalman filter (e.g., the Kalman filter 316). The Kalman filter may beconfigured to output the estimated position of the electronic device102.

The different respective error state metrics may correspond to at leastone of rate gyroscope bias, accelerometer scale factors or accelerometerbias. The first position estimation model may correspond with loweraccuracy and higher acceleration relative to the second positionestimation model. The first position estimation model may be configuredto estimate the position of the electronic device 102 with respect topedestrian motion. The second position estimation model may beconfigured to estimate the position of the electronic device 102 withrespect to vehicle 104 motion.

The electronic device 102 may detect a second predefined condition withrespect to using the second position estimation model to estimate theposition of the electronic device 102. In response to detecting thesecond predefined condition, the electronic device 102 may select toswitch from using the second position estimation model to using thefirst position estimation model to estimate the position of theelectronic device 102. Detecting the second predefined condition maycorrespond with detecting that the electronic device 102 has beendismounted from a mount of the vehicle 104.

FIG. 5 illustrates a flow diagram of another example process forestimating the position of an electronic device in accordance with oneor more implementations. For explanatory purposes, the process 500 isprimarily described herein with reference to the electronic device 102of FIG. 1 . However, the process 500 is not limited to the electronicdevice 102 and one or more blocks (or operations) of the process 500 maybe performed by one or more other components of the electronic device102, and/or by other suitable devices. Further for explanatory purposes,the blocks of the process 500 are described herein as occurring inserial, or linearly. However, multiple blocks of the process 500 mayoccur in parallel. In addition, the blocks of the process 500 need notbe performed in the order shown and/or one or more blocks of the process500 need not be performed and/or can be replaced by other operations.

At block 502, the electronic device 102 determines its accelerationbased on one or more sensor measurements, the electronic device 102being within or coupled to a vehicle in motion (e.g., the vehicle 104).Determining the acceleration of the electronic device 102 may be basedon capturing the one or more sensor measurements within a predefinedtime period (e.g., 3.5 seconds). The acceleration may be correspond tocentrifugal motion of the electronic device 102.

The electronic device 102 calculates, when the acceleration meets apredefined condition (and/or threshold value(s)), a direction of travelfor the electronic device 102 based on a kinematics acceleration modelwhich implements at least one motion-based constraint (504). The atleast one motion-based constraint may correspond to a non-holonomicconstraint for vehicle motion. The electronic device 102 may calculate,when the acceleration does not meet the predefined threshold value, thedirection of travel for the electronic device 102 based on a primarydirection of acceleration for the electronic device 102.

The electronic device 102 estimates a position of the electronic device102 based on the direction of travel for the electronic device 102(506). Estimating the position of the electronic device 102 may befurther based on Global Navigation Satellite System (GNSS) measurements.In addition, estimating the position of the electronic device 102 may bebased on output from a Kalman filter (e.g., the Kalman filter 316). TheKalman filter may be configured to receive device orientation as input,the orientation being based on the direction of travel for theelectronic device 102.

As described above, one aspect of the present technology is thegathering and use of data available from specific and legitimate sourcesfor device orientation initialization. The present disclosurecontemplates that in some instances, this gathered data may includepersonal information data that uniquely identifies or can be used toidentify a specific person. Such personal information data can includedemographic data, position-based data, online identifiers, telephonenumbers, email addresses, home addresses, data or records relating to auser's health or level of fitness (e.g., vital signs measurements,medication information, exercise information), date of birth, or anyother personal information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used fordevice orientation initialization. Accordingly, use of such personalinformation data may facilitate transactions (e.g., on-linetransactions). Further, other uses for personal information data thatbenefit the user are also contemplated by the present disclosure. Forinstance, health and fitness data may be used, in accordance with theuser's preferences to provide insights into their general wellness, ormay be used as positive feedback to individuals using technology topursue wellness goals.

The present disclosure contemplates that those entities responsible forthe collection, analysis, disclosure, transfer, storage, or other use ofsuch personal information data will comply with well-established privacypolicies and/or privacy practices. In particular, such entities would beexpected to implement and consistently apply privacy practices that aregenerally recognized as meeting or exceeding industry or governmentalrequirements for maintaining the privacy of users. Such informationregarding the use of personal data should be prominently and easilyaccessible by users, and should be updated as the collection and/or useof data changes. Personal information from users should be collected forlegitimate uses only. Further, such collection/sharing should occur onlyafter receiving the consent of the users or other legitimate basisspecified in applicable law. Additionally, such entities should considertaking any needed steps for safeguarding and securing access to suchpersonal information data and ensuring that others with access to thepersonal information data adhere to their privacy policies andprocedures. Further, such entities can subject themselves to evaluationby third parties to certify their adherence to widely accepted privacypolicies and practices. In addition, policies and practices should beadapted for the particular types of personal information data beingcollected and/or accessed and adapted to applicable laws and standards,including jurisdiction-specific considerations which may serve to imposea higher standard. For instance, in the US, collection of or access tocertain health data may be governed by federal and/or state laws, suchas the Health Insurance Portability and Accountability Act (HIPAA);whereas health data in other countries may be subject to otherregulations and policies and should be handled accordingly.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof device orientation initialization, the present technology can beconfigured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data duringregistration for services or anytime thereafter. In addition toproviding “opt in” and “opt out” options, the present disclosurecontemplates providing notifications relating to the access or use ofpersonal information. For instance, a user may be notified upondownloading an app that their personal information data will be accessedand then reminded again just before personal information data isaccessed by the app.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing identifiers, controlling the amount orspecificity of data stored (e.g., collecting position data at city levelrather than at an address level), controlling how data is stored (e.g.,aggregating data across users), and/or other methods such asdifferential privacy. s

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data.

FIG. 6 illustrates an example electronic system with which aspects ofthe subject technology may be implemented in accordance with one or moreimplementations. The electronic system 600 can be, and/or can be a partof, any electronic device for generating the features and processesdescribed in reference to FIGS. 1-5 , including but not limited to alaptop computer, tablet computer, smartphone, and wearable device (e.g.,smartwatch, fitness band). The electronic system 600 may include varioustypes of computer readable media and interfaces for various other typesof computer readable media. The electronic system 600 includes one ormore processing unit(s) 614, a permanent storage device 602, a systemmemory 604 (and/or buffer), an input device interface 606, an outputdevice interface 608, a bus 610, a ROM 612, one or more processingunit(s) 614, one or more network interface(s) 616, positioning circuitry618, sensor(s) 620, and/or subsets and variations thereof.

The bus 610 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices of theelectronic system 600. In one or more implementations, the bus 610communicatively connects the one or more processing unit(s) 614 with theROM 612, the system memory 604, and the permanent storage device 602.From these various memory units, the one or more processing unit(s) 614retrieves instructions to execute and data to process in order toexecute the processes of the subject disclosure. The one or moreprocessing unit(s) 614 can be a single processor or a multi-coreprocessor in different implementations.

The ROM 612 stores static data and instructions that are needed by theone or more processing unit(s) 614 and other modules of the electronicsystem 600. The permanent storage device 602, on the other hand, may bea read-and-write memory device. The permanent storage device 602 may bea non-volatile memory unit that stores instructions and data even whenthe electronic system 600 is off. In one or more implementations, amass-storage device (such as a magnetic or optical disk and itscorresponding disk drive) may be used as the permanent storage device602.

In one or more implementations, a removable storage device (such as afloppy disk, flash drive, and its corresponding disk drive) may be usedas the permanent storage device 602. Like the permanent storage device602, the system memory 604 may be a read-and-write memory device.However, unlike the permanent storage device 602, the system memory 604may be a volatile read-and-write memory, such as random access memory.The system memory 604 may store any of the instructions and data thatone or more processing unit(s) 614 may need at runtime. In one or moreimplementations, the processes of the subject disclosure are stored inthe system memory 604, the permanent storage device 602, and/or the ROM612. From these various memory units, the one or more processing unit(s)614 retrieves instructions to execute and data to process in order toexecute the processes of one or more implementations.

The bus 610 also connects to the input and output device interfaces 606and 608. The input device interface 606 enables a user to communicateinformation and select commands to the electronic system 600. Inputdevices that may be used with the input device interface 606 mayinclude, for example, alphanumeric keyboards and pointing devices (alsocalled “cursor control devices”). The output device interface 608 mayenable, for example, the display of images generated by electronicsystem 600. Output devices that may be used with the output deviceinterface 608 may include, for example, printers and display devices,such as a liquid crystal display (LCD), a light emitting diode (LED)display, an organic light emitting diode (OLED) display, a flexibledisplay, a flat panel display, a solid state display, a projector, orany other device for outputting information.

One or more implementations may include devices that function as bothinput and output devices, such as a touchscreen. In theseimplementations, feedback provided to the user can be any form ofsensory feedback, such as visual feedback, auditory feedback, or tactilefeedback; and input from the user can be received in any form, includingacoustic, speech, or tactile input.

The bus 610 also connects to positioning circuitry 618 and sensor(s)620. The positioning circuitry 618 may be used in determining deviceposition based on positioning technology. For example, the positioningcircuitry 618 may provide for one or more of GNSS positioning, wirelessaccess point positioning, cellular phone signal positioning, Bluetoothsignal positioning, image recognition positioning, and/or an INS (e.g.,via motion sensors such as an accelerometer and/or gyroscope).

In one or more implementations, the sensor(s) 620 may be utilized todetect movement, travel and orientation of the electronic system 600.For example, the sensor(s) may include an accelerometer, a rategyroscope, and/or other motion-based sensor(s). Alternatively or inaddition, the sensor(s) 620 may include one or more audio sensors(s)and/or image-based sensor(s) for determining device position and/ororientation. In another example, the sensor(s) 620 may include abarometer which may be utilized to detect atmospheric pressure (e.g.,corresponding to device altitude).

Finally, as shown in FIG. 6 , the bus 610 also couples the electronicsystem 600 to one or more networks and/or to one or more network nodesthrough the one or more network interface(s) 616. In this manner, theelectronic system 600 can be a part of a network of computers (such as aLAN, a wide area network (“WAN”), or an Intranet, or a network ofnetworks, such as the Internet. Any or all components of the electronicsystem 600 can be used in conjunction with the subject disclosure.

Implementations within the scope of the present disclosure can bepartially or entirely realized using a tangible computer-readablestorage medium (or multiple tangible computer-readable storage media ofone or more types) encoding one or more instructions. The tangiblecomputer-readable storage medium also can be non-transitory in nature.

The computer-readable storage medium can be any storage medium that canbe read, written, or otherwise accessed by a general purpose or specialpurpose computing device, including any processing electronics and/orprocessing circuitry capable of executing instructions. For example,without limitation, the computer-readable medium can include anyvolatile semiconductor memory, such as RAM, DRAM, SRAM, T-RAM, Z-RAM,and TTRAM. The computer-readable medium also can include anynon-volatile semiconductor memory, such as ROM, PROM, EPROM, EEPROM,NVRAM, flash, nvSRAM, FeRAM, FeTRAM, MRAM, PRAM, CBRAM, SONOS, RRAM,NRAM, racetrack memory, FJG, and Millipede memory.

Further, the computer-readable storage medium can include anynon-semiconductor memory, such as optical disk storage, magnetic diskstorage, magnetic tape, other magnetic storage devices, or any othermedium capable of storing one or more instructions. In one or moreimplementations, the tangible computer-readable storage medium can bedirectly coupled to a computing device, while in other implementations,the tangible computer-readable storage medium can be indirectly coupledto a computing device, e.g., via one or more wired connections, one ormore wireless connections, or any combination thereof.

Instructions can be directly executable or can be used to developexecutable instructions. For example, instructions can be realized asexecutable or non-executable machine code or as instructions in ahigh-level language that can be compiled to produce executable ornon-executable machine code. Further, instructions also can be realizedas or can include data. Computer-executable instructions also can beorganized in any format, including routines, subroutines, programs, datastructures, objects, modules, applications, applets, functions, etc. Asrecognized by those of skill in the art, details including, but notlimited to, the number, structure, sequence, and organization ofinstructions can vary significantly without varying the underlyinglogic, function, processing, and output.

While the above discussion primarily refers to microprocessor ormulti-core processors that execute software, one or more implementationsare performed by one or more integrated circuits, such as ASICs orFPGAs. In one or more implementations, such integrated circuits executeinstructions that are stored on the circuit itself.

Those of skill in the art would appreciate that the various illustrativeblocks, modules, elements, components, methods, and algorithms describedherein may be implemented as electronic hardware, computer software, orcombinations of both. To illustrate this interchangeability of hardwareand software, various illustrative blocks, modules, elements,components, methods, and algorithms have been described above generallyin terms of their functionality. Whether such functionality isimplemented as hardware or software depends upon the particularapplication and design constraints imposed on the overall system.Skilled artisans may implement the described functionality in varyingways for each particular application. Various components and blocks maybe arranged differently (e.g., arranged in a different order, orpartitioned in a different way) all without departing from the scope ofthe subject technology.

It is understood that any specific order or hierarchy of blocks in theprocesses disclosed is an illustration of example approaches. Based upondesign preferences, it is understood that the specific order orhierarchy of blocks in the processes may be rearranged, or that allillustrated blocks be performed. Any of the blocks may be performedsimultaneously. In one or more implementations, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the implementations described above shouldnot be understood as requiring such separation in all implementations,and it should be understood that the described program components andsystems can generally be integrated together in a single softwareproduct or packaged into multiple software products.

As used in this specification and any claims of this application, theterms “base station”, “receiver”, “computer”, “server”, “processor”, and“memory” all refer to electronic or other technological devices. Theseterms exclude people or groups of people. For the purposes of thespecification, the terms “display” or “displaying” means displaying onan electronic device.

As used herein, the phrase “at least one of” preceding a series ofitems, with the term “and” or “or” to separate any of the items,modifies the list as a whole, rather than each member of the list (i.e.,each item). The phrase “at least one of” does not require selection ofat least one of each item listed; rather, the phrase allows a meaningthat includes at least one of any one of the items, and/or at least oneof any combination of the items, and/or at least one of each of theitems. By way of example, the phrases “at least one of A, B, and C” or“at least one of A, B, or C” each refer to only A, only B, or only C;any combination of A, B, and C; and/or at least one of each of A, B, andC.

The predicate words “configured to”, “operable to”, and “programmed to”do not imply any particular tangible or intangible modification of asubject, but, rather, are intended to be used interchangeably. In one ormore implementations, a processor configured to monitor and control anoperation or a component may also mean the processor being programmed tomonitor and control the operation or the processor being operable tomonitor and control the operation. Likewise, a processor configured toexecute code can be construed as a processor programmed to execute codeor operable to execute code.

Phrases such as an aspect, the aspect, another aspect, some aspects, oneor more aspects, an implementation, the implementation, anotherimplementation, some implementations, one or more implementations, anembodiment, the embodiment, another embodiment, some implementations,one or more implementations, a configuration, the configuration, anotherconfiguration, some configurations, one or more configurations, thesubject technology, the disclosure, the present disclosure, othervariations thereof and alike are for convenience and do not imply that adisclosure relating to such phrase(s) is essential to the subjecttechnology or that such disclosure applies to all configurations of thesubject technology. A disclosure relating to such phrase(s) may apply toall configurations, or one or more configurations. A disclosure relatingto such phrase(s) may provide one or more examples. A phrase such as anaspect or some aspects may refer to one or more aspects and vice versa,and this applies similarly to other foregoing phrases.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration”. Any embodiment described herein as“exemplary” or as an “example” is not necessarily to be construed aspreferred or advantageous over other implementations. Furthermore, tothe extent that the term “include”, “have”, or the like is used in thedescription or the claims, such term is intended to be inclusive in amanner similar to the term “comprise” as “comprise” is interpreted whenemployed as a transitional word in a claim.

All structural and functional equivalents to the elements of the variousaspects described throughout this disclosure that are known or latercome to be known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassed bythe claims. Moreover, nothing disclosed herein is intended to bededicated to the public regardless of whether such disclosure isexplicitly recited in the claims. No claim element is to be construedunder the provisions of 35 U.S.C. § 112(f) unless the element isexpressly recited using the phrase “means for” or, in the case of amethod claim, the element is recited using the phrase “step for”.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more”. Unless specifically statedotherwise, the term “some” refers to one or more. Pronouns in themasculine (e.g., his) include the feminine and neuter gender (e.g., herand its) and vice versa. Headings and subheadings, if any, are used forconvenience only and do not limit the subject disclosure.

What is claimed is:
 1. A method, comprising: determining, by a device,that the device is within or coupled to a vehicle in motion; selecting,by the device in response to the determining, to use a first positionestimation model to estimate a position of the device; estimating, bythe device, the position of the device using the first positionestimation model; detecting, by the device, a predefined condition withrespect to using the first position estimation model to estimate theposition of the device; selecting, by the device in response todetecting the predefined condition, to switch from using the firstposition estimation model to using a second position estimation model toestimate the position of the device while the device is within orcoupled to the vehicle in motion; and estimating, by the device, theposition of the device using the second position estimation model,wherein the first and second position estimation models apply differentrespective error state metrics to estimate the position of the device.2. The method of claim 1, wherein using the first position estimationmodel provides for initializing an orientation for the device, theorientation including a heading of the device.
 3. The method of claim 1,wherein each of the first and second position estimation models isconfigured to estimate the position of the device based on GlobalNavigation Satellite System (GNSS) measurements.
 4. The method of claim1, wherein each of the first and second position estimation models isimplemented within a Kalman filter, and wherein the Kalman filter isconfigured to output the estimated position of the device.
 5. The methodof claim 1, wherein the different respective error state metricscorrespond to at least one of rate gyroscope bias, accelerometer scalefactors or accelerometer bias.
 6. The method of claim 1, wherein thefirst position estimation model provides lower accuracy relative to thesecond position estimation model.
 7. The method of claim 1, wherein thefirst position estimation model is configured to estimate the positionof the device with respect to pedestrian motion, and wherein the secondposition estimation model is configured to estimate the position of thedevice with respect to vehicle motion.
 8. The method of claim 1, whereindetecting the predefined condition corresponds with detecting that apredetermined amount of time has passed with respect to using the firstposition estimation model to estimate the position of the device.
 9. Themethod of claim 1, wherein detecting the predefined conditioncorresponds with detecting that Global Navigation Satellite System(GNSS) measurements corresponding to device position converge withKalman filter output corresponding to device position.
 10. The method ofclaim 1, further comprising: detecting a second predefined conditionwith respect to using the second position estimation model to estimatethe position of the device; and selecting, in response to detecting thesecond predefined condition, to switch from using the second positionestimation model to using the first position estimation model toestimate the position of the device.
 11. The method of claim 10, whereindetecting the second predefined condition corresponds with detectingthat the device has been dismounted from a mount of the vehicle orremoved from the vehicle.
 12. The method of claim 1, wherein using thefirst position estimation model is based on an initial heading of thedevice, the initial heading of the device having been determined basedon a kinematics acceleration model which implements at least onemotion-based constraint.
 13. A computer program product comprising codestored in a non-transitory computer-readable storage medium, the codecomprising: code to determine that a device is within or coupled to avehicle in motion; code to select, in response to the determining, touse a first position estimation model to estimate a position of thedevice; code to estimate the position of the device using the firstposition estimation model; code to detect a predefined condition withrespect to using the first position estimation model to estimate theposition of the device; code to select, in response to detecting thepredefined condition, to switch from using the first position estimationmodel to using a second position estimation model to estimate theposition of the device while the device is within or coupled to thevehicle in motion; and code to estimate the position of the device usingthe second position estimation model, wherein the first and secondposition estimation models apply different respective error statemetrics to estimate the position of the device.
 14. A device,comprising: at least one processor; and a memory including instructionsthat, when executed by the at least one processor, cause the at leastone processor to: determine that a device is within or coupled to avehicle in motion; estimate, in response to the determining, a positionof the device using a first position estimation model; detect apredefined condition with respect to using the first position estimationmodel to estimate the position of the device; and switch, in response tothe detection of the predefined condition, from using the first positionestimation model to using a second position estimation model to estimatethe position of the device while the device is within or coupled to thevehicle in motion, wherein the first and second position estimationmodels apply different respective error state metrics to estimate theposition of the device.
 15. The device of claim 14, wherein the at leastone processor is configured to use the first position estimation modelto initialize an orientation for the device, the orientation including aheading of the device.
 16. The device of claim 14, wherein each of thefirst and second position estimation models is implemented within aKalman filter, and wherein the Kalman filter is configured to output theestimated position of the device.
 17. The device of claim 14, whereinthe different respective error state metrics correspond to at least oneof rate gyroscope bias, accelerometer scale factors or accelerometerbias.
 18. The device of claim 14, wherein the first position estimationmodel provides lower accuracy relative to the second position estimationmodel.
 19. The device of claim 14, wherein the first position estimationmodel is configured to estimate the position of the device with respectto pedestrian motion, and wherein the second position estimation modelis configured to estimate the position of the device with respect tovehicle motion.
 20. The device of claim 14, wherein the predefinedcondition comprises a predetermined amount of time passed with respectto using the first position estimation model to estimate the position ofthe device.